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Summary

This study enhances financial early warning models for listed companies by incorporating earnings management indicators and logistic regression. The improved model boosts prediction accuracy, significantly reducing bankruptcy risks for investors.

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Area of Science:

Financial Risk Management
Computational Finance
Corporate Governance

Background:

Financial crises in listed companies cause substantial investor losses.
Effective financial early warning systems are crucial for stakeholders.
Earnings management is a prevalent issue in listed companies, impacting financial reporting accuracy.

Purpose of the Study:

To develop an enhanced financial early warning model for listed companies.
To improve the accuracy and reliability of financial distress prediction.
To mitigate risks associated with corporate financial instability.

Main Methods:

Utilized a forward neural network model optimized with particle swarm optimization (PSO).
Integrated quantitative earnings management indicators into the predictive model.
Applied logistic regression for further model refinement and validation.

Main Results:

The model incorporating earnings management indicators improved accuracy from 65% to 70%.
Further modification using logistic regression enhanced model accuracy to 75%.
The combined approach demonstrated a significant improvement in predicting financial distress.

Conclusions:

Incorporating earnings management indicators and logistic regression significantly boosts the accuracy of PSO-based financial early warning models.
The enhanced model effectively reduces the risk of bankruptcy and liquidation for companies facing financial difficulties.
This research provides a more robust tool for investors and stakeholders to assess corporate financial health.